Conclusions and Future recommendations
CHAPTER 6 4. The model predicted the experimental values of the validation dataset with an
average error value of 14.97%.
The observations from this objective emphasized the significance of developing a reliable process model to understand the process thoroughly. The procedure for model development and exploring the associated components of the same was thus demonstrated for the production of Ranibizumab from recombinant E. coli through this objective. The availability of a validated process model facilitates the opportunity to explore the implementation of optimization and control strategies to enhance the process efficiency, and this was focused on the following objective.
6.4 Optimization studies for maximizing biomass production and predicting harvest time
The third objective presented in this thesis focused on implementing different optimization strategies for enhancing the productivity of the protein of interest and improving the various aspects of the production process. The physiological properties of the biomass greatly influence productivity, and therefore, developing optimization strategies based on biomass concentration is significant and can aid in improving the process. Two case studies were formulated; case study (1) focused on maximizing the total biomass (XV) by simultaneously minimizing the reactor's broth volume (V). The two objectives, f(1) and f(2) were combined to formulate a multiobjective optimization (MOO) problem, which was then solved using different algorithms like sequential quadratic programming (SQP) and genetic algorithm (GA). Case study (2) aimed to explore the impact of different values of harvest time (tend) along with the MOO from the case study (1). The outcome of this study was extended to demonstrate a procedure to calculate the profit function from the biotherapeutic production process. The significant outcomes from this chapter are highlighted as follows.
1. Simulation studies were carried out to choose the constraints for the manipulating variable and test the single objective optimization for f(1) and f(2) individually.
Different time interval cases (equal and variable) were also simulated, and an equal time interval of 10 min (E2) was chosen for the first case study.
2. The MOO was solved using both the SQP and GA, and they yielded maximum total biomass (XV) of 58.8 and 58.55 g for a minimum volume (V) of 1.96 L, respectively.
6.5 Recommendations for the future
3. A comparison of the objective function values at different λ values was presented, and the corresponding optimal substrate feeding profile was obtained.
4. The optimization results from the case study (1) showed that the total biomass obtained from the optimal substrate feeding profile was 20.6% higher than the total biomass from the experimental studies.
5. Among SQP and GA, SQP was found to be capable of solving the optimization problem three times faster, which is required for real-time application.
6. The developed optimization methodology was capable of handling faults in the actuator, as the objective function increased < 2% in the tested cases, and thus, the developed optimization methodology was validated.
7. A sample procedure to select optimal points for given cost factors was demonstrated.
8. Simulation studies were carried out with different tend values to predict the optimal fed-batch harvest time.
9. The reactor could be operated at a suitable harvest time depending on the operation volume by choosing a practically feasible optimal feeding profile.
10. It was observed that a harvest time of 24 h might be beneficial for the current study, according to the Pareto results obtained from both methodologies (SQP and Pareto search) explored in this work.
The results from the third objective projected the possibility of achieving enhanced productivity by operating the reactor according to the optimal substrate feeding profile achieved from the optimization studies. Further, operating the reactor according to the optimal fed-batch harvest time presented in this study can streamline the therapeutic production process, yielding great economic benefits.
6.5 Recommendations for the future
The work presented in this thesis successfully demonstrated the application of measurement, modelling and optimization strategies to improve the process performance of a therapeutic protein production process. The significant accomplishments of this work can be summarized as follows.
• The proposed Cole-Cole model could be applied for real-time estimation of the physiological properties, thereby enabling the operator to take real-time process decisions.
CHAPTER 6
• The dynamic parameter estimation and the MOO implemented in this study would be beneficial for upscaling the therapeutic protein production at an industrial scale.
• The optimization studies for biomass maximization and harvest time predictions can improve process monitoring and product quality.
Based on the observations and outcomes outlined in this thesis, the following few suggestions are provided for carrying out additional research along similar lines. The recommendations which have the scope to be pursued for future research can be briefed as follows.
• Integration and implementation of the proposed Cole-Cole methodology in real- time could be carried out in future studies. The methodology for real-time estimation of physiological properties was proposed based on the validation of the Cole-Cole model presented in this study. This proposal can be implemented in real-time, and the application of dielectric spectroscopy to make real-time decisions based on the observed physiological changes can be explored.
• Relevant kinetic models other than Monod based dual kinetics can be explored for describing the biomass growth kinetics. Additionally, the mechanistic model can be extended to include other relevant state variables like by-product formation, and measurements from other process analyzers can also be incorporated into the process model. The overall dynamic model validation strategy could be extended for other biotherapeutic protein production processes.
• The experimental validation of the optimization results reported from this work can be explored. Reactor studies can be carried out using the optimal dynamic feed profile furnished from the presented optimization results, and the enhancement in total biomass concentration and productivity can be observed.
Similarly, the validation studies to harvest the fed-batch cultivation at suitable optimal harvest time can be carried out to verify the practical feasibility of the optimal results achieved.
• The validated process model presented in this study can be utilized for developing appropriate process control strategies wherein the choice of different manipulating variables can be explored. The proposed methodology for real-time biomass estimation can be employed to measure the critical process variable, and
6.5 Recommendations for the future
developing a control strategy based on the same can be investigated. The application of various control strategies can significantly enhance productivity, and therefore, the presented measurement and modelling strategies can be extended for the development of the same.
• Strategies and methodologies associated with the measurement using advanced PAT tool, development of reliable process model, and formulation of different optimization strategies described in this work could be taken forward for their implementation in other relevant therapeutic production processes.
• With the growing interest in the development of 'digital twins' to serve as a replica of the underlying bioprocesses, the integration of data from advanced PAT tools can be taken forward to develop better process models that can enable the implementation of advanced real-time optimization and control strategies.
• The advancements in the field of data science can be exploited to enable efficient noise treatment and obtain more accurate process measurements. Additionally, the possibility of developing data-driven and hybrid models for the interpretation of the process measurements and obtaining a reliable representation of the fermentation process can be explored.